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bioRxiv · 10.1101/2024.08.07.607107

Generating Multi-state Conformations of P-type ATPases with a Diffusion Model

Xu, J.·Wang, Y.
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Abstract

Understanding and predicting the diverse conformational states of membrane proteins is essential for elucidating their biological functions. Despite advancements in computational methods, accurately capturing these complex structural changes remains a significant challenge. Here we introduce a computational approach to generate diverse and biologically relevant conformations of membrane proteins using a conditional diffusion model. Our approach integrates forward and backward diffusion processes, incorporating state classifiers and additional conditioners to control the generation gradient of conformational states. We specifically targeted the P-type ATPases, a critical family of membrane transporters, and constructed a comprehensive dataset through a combination of experimental structures and molecular dynamics simulations. Our model, incorporating a graph neural network with specialized membrane constraints, demonstrates exceptional accuracy in generating a wide range of P-type ATPase conformations associated with different functional states. This approach represents a meaningful step forward in the computational generation of membrane protein conformations using AI and holds promise for studying the dynamics of other membrane proteins. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/607107v2_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@2a180aorg.highwire.dtl.DTLVardef@19061a3org.highwire.dtl.DTLVardef@1fa53b5org.highwire.dtl.DTLVardef@18db2f0_HPS_FORMAT_FIGEXP M_FIG C_FIG

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Xu, J., Wang, Y.. 2024-08-09. Generating Multi-state Conformations of P-type ATPases with a Diffusion Model. https://doi.org/10.1101/2024.08.07.607107

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